Franziska Boenisch

CISPA Helmholtz Center

Franziska is a tenure-track faculty at the CISPA Helmholtz Center for Information Security, where she co-leads the SprintML lab. Her research focuses on private and trustworthy machine learning; during her Ph.D. at Freie Universität Berlin and Fraunhofer AISEC she pioneered the notion of individualized privacy in ML. Before joining CISPA, she was a Postdoctoral Fellow at the University of Toronto and the Vector Institute. She received an ERC Starting Grant in 2025 for research on privacy in foundation models and has been recognized with the Fraunhofer ICT Dissertation Award (2023), a GI Junior Fellowship (2024), and a Werner-von-Siemens Fellowship (2025).

Dominik Janzing

Amazon

Dominik Janzing is a Principal Research Scientist at Amazon in Tübingen, Germany, where he works on new approaches to causal inference for cloud computing, for example in root cause analysis of anomalies. He is particularly interested in the foundations of causal inference and in defining formal concepts that are both practically useful and domain-independent. He also teaches seminars on causality at the Karlsruhe Institute of Technology.

Wojciech Samek

TU Berlin / Fraunhofer HHI

Wojciech Samek is a Professor in the EECS Department at the Technical University of Berlin and Head of the AI Department at the Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany. He earned a Dipl.-Inf. degree in Computer Science from Humboldt University of Berlin in 2010 and a Ph.D. (with honors) from TU Berlin in 2014. After his doctorate, he founded the Machine Learning Group at Fraunhofer HHI, which became an independent department in 2021. He is a Fellow at BIFOLD – the Berlin Institute for the Foundation of Learning and Data – and the ELLIS Unit Berlin. He serves as a member of Germany’s Platform for AI and sits on advisory and executive boards including the WUT Center for Credible AI, AGH University’s AI Center, IDEAS Research Institute, HEIBRiDS, and the DAAD Konrad Zuse School ELIZA. His research focuses on explainable AI (XAI), spanning methods, theory, and applications, with pioneering contributions including Layer-wise Relevance Propagation (LRP), concept-level explainability, evaluation of explanations, and XAI-driven model and data improvement. He has served as Senior Editor of IEEE TNNLS, Associate Editor for several journals, and Area Chair at NeurIPS, ICML, and NAACL. He has received multiple best paper awards (Pattern Recognition 2020, Digital Signal Processing 2022, IEEE SPS 2024, Information Fusion 2025), has co-authored over 250 peer-reviewed papers, and was recognized as a Highly Cited Researcher 2025 by Clarivate.

Adam Dziedzic

CISPA Helmholtz Center

Adam is a faculty member at CISPA Helmholtz Center for Information Security, co-leading the SprintML group. His research is focused on secure and trustworthy Machine Learning. Adam was a Postdoctoral Fellow at the Vector Institute and the University of Toronto, and a member of the CleverHans Lab, advised by Prof. Nicolas Papernot. He earned his PhD at the University of Chicago, where he was advised by Prof. Sanjay Krishnan and worked on input and model compression for adaptive and robust neural networks. Adam obtained his Bachelor's and Master's degrees from the Warsaw University of Technology in Poland. He was also studying at DTU (Technical University of Denmark)and carried out research at EPFL, Switzerland. Adam also worked at CERN (Geneva, Switzerland), Barclays Investment Bank in London (UK), Microsoft Research (Redmond, USA), and Google (Madison, USA).

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